Data processing method and apparatus, and storage medium

ABSTRACT

A data processing method includes obtaining sample data of event execution of a game client, and performing preprocessing on the sample data to obtain a plurality of layers of data combinations. Each layer of the plurality of layers of data combinations corresponds to a target event object in a same target event, different layers correspond to different target event objects in the target event, and the target event objects are event objects on the game client to be executed concurrently. The method also includes performing processing on each layer of data combinations according to a preset processing algorithm, to obtain a processing result of each layer of data combinations, and performing consolidation processing on the processing result to obtain a target instruction. The target instruction is used for instructing the game client to concurrently execute the different target event objects corresponding to the different layers of data combinations.

RELATED APPLICATIONS

This application is a continuation application of PCT Patent ApplicationNo. PCT/CN2017/102702, filed on Sep. 21, 2017, which claims priority toChinese Patent Application No. 2016108388048, entitled “DATA PROCESSINGMETHOD AND APPARATUS” filed with the Chinese Patent Office on Sep. 21,2016, which is incorporated by reference in its entirety.

FIELD OF TECHNOLOGY

The present disclosure relates to the field of data processing and,specifically, to a data processing method and apparatus, and a storagemedium.

BACKGROUND

Currently, the data processing rule of a turn-based event is relativelysimple. Objects for executing the event have global information aboutthe event, take actions in turns, and have a relatively long decisiontime for the event. The behavior of the event is conducted right away,or is determined by a feedback. For example, the turn-based event is aturn-based game. The rule of the game is relatively simple, game playershave global information, take actions in turns, and have a relativelylong decision time. The behavior may be conducted right away, or may bedetermined according to a feedback. For example, the game is the game ofGo.

The data processing rule of a real-time event is complex. Objects forexecuting the real-time event only have part of information about theevent, take actions concurrently, and have a relatively short decisiontime. Conduction of a behavior of the event requires time and there is asuccess probability. Therefore, there are differences between dataprocessing of a real-time event and data processing of a turn-basedevent.

Among turn-based events, AlphaGo, an intelligent program of the game ofGo is an algorithm that implements a high-level move of the game of Goby training a policy network and a value network by using deep learningand consolidating the policy network and the value network by using aMonte Carlo tree. FIG. 1 is a schematic structural diagram of a policynetwork and a value network of an AlphaGo algorithm according to therelated art. As shown in FIG. 1, the AlphaGo algorithm trains the policynetwork and the value network by using deep learning. A human expertside (human expert position) transmits a policy network (SL network) ofthe human expert side to an artificial intelligence (AI) side (self-playposition) by using a classification rollout policy and a policyalgorithm (policy gradient). The AI side trains a policy network (RLnetwork) and a value network of the AI side, to obtain data. The policynetwork of the human expert side and the policy network of the AI sideare collectively referred to as a policy network, and the policy networkand the value network are trained according to an algorithm formula andare implemented by using a Monte Carlo tree search (MCTS) algorithm.

FIG. 2 is a schematic diagram of an MCTS algorithm according to therelated art. As shown in FIG. 2, a move probability is selected and aquantity of move samples is extended by using a policy network. Abenefit of a current move is evaluated and a result of the benefit ofthe current move is fed back by using a value network. The moveprobability selected by the policy network and the profit of the currentmove evaluated by the value network are consolidated and simulated andan optimal move position is finally selected according to the currentgame state by using the MCTS algorithm.

However, data processing of a real-time event is far more complex thanthe data processing of the above turn-based event. Because there are arelatively large number of differences between data processing of aturn-based event and data processing of a real-time event, the way ofcombining two layers of networks in an algorithm cannot satisfy therequirement of a macro decision of a real-time event, let alone therequirement of a micro operation and, therefore, cannot satisfy therequirement of a real-time event intelligent system, causing a low dataprocessing efficiency.

The disclosed methods and systems are directed to solve one or moreproblems set forth above and other problems.

SUMMARY

The embodiments of the present invention provide a data processingmethod and apparatus, and a storage medium, so as to at least resolvethe technical problem of a low data processing efficiency.

According to one aspect of the embodiments of the present invention, adata processing method is provided. The data processing method includes:obtaining sample data of event execution of a game client, andperforming preprocessing on the sample data to obtain a plurality oflayers of data combinations. Each layer of the plurality of layers ofdata combinations corresponds to a target event object in a same targetevent, different layers of the plurality of layers of data combinationscorrespond to different target event objects in the target event, andthe target event objects are event objects on the game client to beexecuted concurrently. The method also includes performing processing oneach layer of data combinations according to a preset processingalgorithm, to obtain a processing result of each layer of datacombinations; and performing consolidation processing on the processingresult of each layer of data combinations to obtain a targetinstruction. The target instruction is used for instructing the gameclient to concurrently execute the different target event objectscorresponding to the different layers of data combinations.

According to another aspect of the embodiments of the present invention,a data processing system is provided. The data processing systemincludes a memory storing computer program instructions; and one or moreprocessors coupled to the memory. When executing the computer programinstructions, the one or more processors are configured to perform:obtaining sample data of event execution of a game client; andperforming preprocessing on the sample data to obtain a plurality oflayers of data combinations. Each layer of the plurality of layers ofdata combinations corresponds to a target event object in a same targetevent, different layers of the plurality of layers of data combinationscorrespond to different target event objects in the target event, andthe target event objects are event objects on the game client to beexecuted concurrently. The one or more processors are also configured toperform: performing processing on each layer of data combinationsaccording to a preset processing algorithm, to obtain a processingresult of each layer of data combinations; and performing consolidationprocessing on the processing result of each layer of data combinationsto obtain a target instruction. The target instruction is used forinstructing the game client to concurrently execute the different targetevent objects corresponding to the different layers of datacombinations.

According to another aspect of the embodiments of the present invention,a non-transitory computer-readable storage medium is further provided.The non-transitory computer-readable storage medium stores computerprogram instructions executable by at least one processor to perform:obtaining sample data of event execution of a game client; andperforming preprocessing on the sample data to obtain a plurality oflayers of data combinations. Each layer of the plurality of layers ofdata combinations corresponds to a target event object in a same targetevent, different layers of the plurality of layers of data combinationscorrespond to different target event objects in the target event, andthe target event objects are event objects on the game client to beexecuted concurrently. The non-transitory computer-readable storagemedium further stores computer program instructions executable by atleast one processor to perform: performing processing on each layer ofdata combinations according to a preset processing algorithm, to obtaina processing result of each layer of data combinations; and performingconsolidation processing on the processing result of each layer of datacombinations to obtain a target instruction. The target instruction isused for instructing the game client to concurrently execute thedifferent target event objects corresponding to the different layers ofdata combinations.

Other aspects of the present disclosure can be understood by thoseskilled in the art in light of the description, the claims, and thedrawings of the present disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings described herein are used to provide furtherunderstanding of the present disclosure, and form part of thisapplication. Exemplary embodiments of the present invention anddescriptions thereof are used to explain the present disclosure, and donot constitute any inappropriate limitation to the present disclosure.In the figures:

FIG. 1 is a schematic structural diagram of a policy network and a valuenetwork of an AlphaGo algorithm;

FIG. 2 is a schematic diagram of an MCTS algorithm;

FIG. 3 is a schematic diagram of a hardware environment of a dataprocessing method according to an embodiment of the present disclosure;

FIG. 4 is a flowchart of a data processing method according to anembodiment of the present disclosure;

FIG. 5 is a flowchart of a method of tagging sample data according to aplurality of sample sequences of the sample data according to anembodiment of the present disclosure;

FIG. 6 is a flowchart of a method of performing preprocessing on taggedsample data according to an embodiment of the present disclosure;

FIG. 7 is a flowchart of another data processing method according to anembodiment of the present disclosure;

FIG. 8 is a flowchart of another data processing method according to anembodiment of the present disclosure;

FIG. 9 is a flowchart of a method of performing processing on sampleinformation on each layer of data combination according to a processingalgorithm corresponding to each layer of a plurality of layers of datacombinations according to an embodiment of the present disclosure;

FIG. 10 is a flowchart of another data processing method according to anembodiment of the present disclosure;

FIG. 11 is a flowchart of another data processing method according to anembodiment of the present disclosure;

FIG. 12 is a schematic diagram of an interaction process in a gameprocess according to an embodiment of the present disclosure;

FIG. 13 is a flowchart of another method of tagging sample dataaccording to a plurality of sample sequences of the sample dataaccording to an embodiment of the present disclosure;

FIG. 14 is a flowchart of another data processing method according to anembodiment of the present disclosure;

FIG. 15 is a schematic flowchart of a game interaction method accordingto an embodiment of the present disclosure;

FIG. 16 is a schematic diagram of a data processing apparatus accordingto an embodiment of the present disclosure;

FIG. 17 is a schematic diagram of another data processing apparatusaccording to an embodiment of the present disclosure;

FIG. 18 is a schematic diagram of another data processing apparatusaccording to an embodiment of the present disclosure;

FIG. 19 is a schematic diagram of another data processing apparatusaccording to an embodiment of the present disclosure;

FIG. 20 is a schematic diagram of another data processing apparatusaccording to an embodiment of the present disclosure;

FIG. 21 is a schematic diagram of another data processing apparatusaccording to an embodiment of the present disclosure;

FIG. 22 is a schematic diagram of another data processing apparatusaccording to an embodiment of the present disclosure;

FIG. 23 is a schematic diagram of another data processing apparatusaccording to an embodiment of the present disclosure; and

FIG. 24 is a structural block diagram of a terminal according to anembodiment of the present disclosure.

DETAILED DESCRIPTION

To help persons skilled in the art understand better the solutions inthe present disclosure, the following describes the technical solutionsin the embodiments of the present disclosure with reference to theaccompanying drawings in the embodiments of the present disclosure.Apparently, the described embodiments are merely some but not all of theembodiments of the present disclosure. Other embodiments obtained bypersons of ordinary skill in the art based on the disclosed embodimentsof the present disclosure without creative efforts shall fall within theprotection scope of the present disclosure.

It should be noted that, in the specification, claims, and accompanyingdrawings of the present disclosure, the terms “first”, “second”, and thelike are intended to distinguish between similar objects instead of aspecific order or sequence. It should be understood that the data termedin such a way are interchangeable in proper circumstances so that theembodiments of the present disclosure described herein can beimplemented in orders other than the order illustrated or describedherein. Moreover, the terms “include”, “contain” and any other variantsmean to cover the non-exclusive inclusion. For example, a process,method, system, product, or device that includes a list of steps orunits is not necessarily limited to those steps or units expresslylisted, but may include other steps or units not expressly listed orinherent to such a process, method, product, or device.

According to an embodiment of the present disclosure, a data processingmethod is provided. The data processing method may be applied to ahardware environment. FIG. 3 is a schematic diagram of a hardwareenvironment of a data processing method according to an embodiment ofthe present disclosure.

As shown in FIG. 3, the hardware environment includes a server 302 and aterminal 304. The server 302 is connected to the terminal 304 by using anetwork. The network includes a wide area network, a metropolitan areanetwork or a local area network. The terminal 304 is not limited to apersonal computer (PC), a mobile phone, a tablet computer, or the like.The data processing method may be performed by the server 302, or may beperformed by the terminal 304, or may be performed by the server 302together with the terminal 304. That the data processing method in thisembodiment of the present disclosure is performed by the terminal 304may be that the data processing method is performed by a clientinstalled on the terminal 304.

FIG. 4 is a flowchart of a data processing method according to anembodiment of the present disclosure. As shown in FIG. 4, the dataprocessing method may include the followings.

S402: Obtaining sample data of event execution of a game client.

The game client is used for executing an event, for example, executingan event in a human-computer battle mode in a real-time game. Thereal-time game is different from a turn-based game.

Data is generated during the event execution of the game client. Thedata may be game data. The sample data is a part of data actuallyobserved or surveyed during the event execution of the game client, andmay be data randomly extracted, having an adequate quantity and capableof reflecting a general situation of the event execution of the gameclient. In one embodiment, alternatively, the data generated during theevent execution of the game client may be properly constructed by usinga multi-layer deep learning framework, to obtain the sample data.

Optionally, when sample data of event execution of a game client isobtained, the sample data is an input sample and may be a game sample.The game sample includes a plurality of sample sequences. The pluralityof sample sequences has different priorities, and the sample sequenceshaving different priorities may include a same data frame.

S404: Performing preprocessing on the sample data to obtain a pluralityof layers of data combinations.

That is, preprocessing is performed on the sample data to obtain aplurality of layers of data combinations, each layer of the plurality oflayers of data combinations corresponding to a target event object in asame target event, different layers of the plurality of layers of datacombinations corresponding to different target event objects in thetarget event, and the target event objects being event objects on thegame client to be executed concurrently.

Because there is a large number of design dimensions of game data, thegame data cannot be directly used as training data. After the sampledata of the event execution of the game client is obtained, a pluralityof sample sequences of the sample data is obtained, and the sample datais tagged according to the plurality of sample sequences, to obtaintagged sample data. The sample data may be tagged by using a presettagging logic. Optionally, the entire sample is tagged according to acharacteristic sequence of the input sample and according to the presetlogical configuration, to obtain the tagged sample data.

Because of complexity of data processing rules, a plurality of samplesequences may have a same data frame, that is, a same data frame maybelong to a plurality of sample sequences. The priorities of theplurality of sample sequences may be determined according to acharacteristic of the event. Different sample sequences are taggedaccording to an order of the priorities of the sample sequences. Thesample sequences may be tagged by using a preset rule or a preset samplesegmentation algorithm, to obtain a tagging frame. All tagging framesare traversed and neighboring and same tagging frames are tagged intoone sample sequence. A start frame and an end frame of each samplesequence are tagged, to obtain the tagged sample data.

After the sample data is tagged according to the plurality of samplesequences of the sample data to obtain tagged sample data, preprocessingis performed on the tagged sample data to obtain a plurality of layersof data combinations. The plurality of layers of data combinations maybe formed by assembling state information extracted from the taggedsample data by using a common state function. For example, current eventstate information may be extracted from the tagged sample data by usinga common state function. The state information is assembled to form aplurality of layers of data combinations. The target event objects areevent objects on the game client to be executed concurrently, and maycorrespond to an own-side character state, a friendly-side characterstate, adversary-side lethality information, map information, non-playercharacter (NPC) information, and the like.

Each layer of the plurality of layers of data combinations in oneembodiment corresponds to a target event object in a same target event,and different layers of the plurality of layers of data combinationscorrespond to different target event objects in the target event. Thatis, there is a one-to-one correspondence between each layer of theplurality of layers of data combinations and a target event object in asame target event. For example, the target event is a battle event, andthe target event objects include an A event object, a B event object,and a C event object to be executed concurrently. The A event object,the B event object, and the C event object are different, and a firstlayer of data combination corresponds to the A event object in thebattle event, a second layer of data combination corresponds to the Bevent object in the battle event, and a third layer of data combinationcorresponds to the C event object in the battle event.

For another example, the event objects to be executed concurrentlyinclude an own-side character, a friendly-side character, anadversary-side character, and an NPC. Each layer of data combinationcorresponds to the own-side character, the friendly-side character, theadversary-side character, the NPC, and the like. Different layers of theplurality of layers of data combinations in one embodiment correspond todifferent target event objects in the target event. For example, in areal-time game in which there are five persons in each party, own-sidecharacter states are placed on a first layer; friendly-side characterstates are ranked according to strength and placed on a second layer toa fifth layer. That is, on the second layer to the fifth layer, thefriendly-side characters are differentiated according to the strength;adversary-side characters are ranked according to lethality and placedon a sixth layer to a tenth layer. That is, on the sixth layer to thetenth layer, the adversary-side characters are differentiated accordingto the lethality; map information and NPC information are placed on anoutermost layer. Other data assembling principles are also applicable,and are not limited herein.

The state information of the sample data and each piece of characterdata on each layer of data combination are mapped to a legal actionspace according to a game rule state, to obtain event data. The stateinformation, the character data, and the event data on each layer ofdata combination are assembled to form sample information on each layerof data combination. Rotation processing is performed on the sampledata, to extend the sample quantity corresponding to the sample data.Other user information may also be added to the sample information. Forexample, an error rate and an operating frequency of the event executionare added to the sample information, thereby facilitating training. Inthis way, the plurality of layers of data combinations is obtained byperforming preprocessing on the tagged sample data.

S406: Performing processing on each layer of data combination accordingto a preset processing algorithm, to obtain a processing result of eachlayer of data combination.

Each layer of the plurality of layers of data combinations has acorresponding preset processing algorithm. The preset processingalgorithm may be learning, on each layer of data combination accordingto the sample information, a probability model of an executionprobability of an event and a value model of an execution value of theevent in the current state information. For example, each of currentevent state information, character information, and a character in asample is mapped to a corresponding numerical value in a legitimateaction space according to a game rule state, to learn an executionprobability model of an action and a value model of the action in thecurrent state. A specific algorithm may be an MCTS algorithm used inAlphaGo for consolidating a policy network and a value network.

The MCTS algorithm is a heuristic search algorithm for decision makingand selects a most profitable behavior by extending a search tree andsimulation, thereby making an optimal decision and obtaining aprocessing result of each layer of data combination. The processingresult is output on a decision layer for performing consolidationprocessing and is a result obtained by performing processing on eachlayer of data combination according to a preset processing algorithm.The processing result includes an execution probability and an executionvalue. Processing may be performed on the sample information on eachlayer of data combination according to a preset probability model and apreset value model corresponding to each layer of data combination, toobtain an execution probability and an execution value that are ofexecution of the target event of the game client and that correspond toeach layer of data combination. For example, the preset probabilitymodel is an execution probability model of an action, and the presetvalue model is a value model of the action. Processing is performed onthe sample information on each layer of data combination according tothe execution probability model of the action and the value model of theaction in a current state, to obtain the execution probability and theexecution value of execution of the target event of the game client andcorresponding to each layer of data combination.

S408: Performing consolidation processing on the processing result ofeach layer of data combination to obtain a target instruction.

That is, consolidation processing is performed on the processing resultof each layer of data combination to obtain a target instruction, andthe target instruction is used for instructing the game client toconcurrently execute the different target event objects corresponding tothe different layers of data combinations.

After the processing result of each layer of data combination isobtained by performing processing on the sample information on eachlayer of data combination, weighted consolidation is performed on theprocessing result of each layer of data combination, to obtain a targetinstruction. The target instruction is a final policy used forinstructing the game client to concurrently execute the different targetevent objects corresponding to the different layers of datacombinations. A state evaluation function may be added to the targetinstruction, to determine whether the target instruction on the currentboard needs to be changed, so as to satisfy various event executionenvironments.

Optionally, after the target instruction is obtained by performingconsolidation processing on the processing result of each layer of datacombination, the target instruction is executed, that is, the finalpolicy is executed. During execution of the target instruction, thestate evaluation function may be added, to determine whether the policyon the current board needs to be changed, so as to satisfy various gameenvironments.

Optionally, during the execution the target instruction, current gamestate information of the game client may be displayed. The current gamestate information is an execution result of the target instruction.Whether the target instruction needs to be updated is determinedaccording to the preset state evaluation function and the currentexecution result, and whether the target instruction needs to be updatedmay be determined by using a behavior tree. The behavior tree is agraphical modeling language and is used for describing differentexecution conditions and manners of a behavior in a game, therebyensuring fast execution of the behavior and improving experience of agame player.

Thus, according to S402 to S408, sample data of event execution of agame client is obtained; preprocessing is performed on the sample datato obtain a plurality of layers of data combinations, each layer of theplurality of layers of data combinations corresponding to a target eventobject in a same target event, different layers of the plurality oflayers of data combinations corresponding to different target eventobjects in the target event, and the target event objects being eventobjects on the game client to be executed concurrently; processing isperformed on the sample information on each layer of data combinationaccording to the processing algorithm corresponding to each layer of theplurality of layers of data combinations, to obtain the processingresult of each layer of data combination; and consolidation processingis performed on the processing result of each layer of data combinationto obtain a target instruction, the target instruction being used forinstructing the game client to concurrently execute the different targetevent objects corresponding to the different layers of datacombinations. In this way, a technical problem of a low data processingefficiency is resolved, and a technical effect of improving dataprocessing efficiency is achieved.

Optionally, in S404, the performing preprocessing on the sample data toobtain a plurality of layers of data combinations includes: tagging thesample data according to a plurality of sample sequences of the sampledata, to obtain tagged sample data; and performing preprocessing on thetagged sample data to obtain the plurality of layers of datacombinations, different layers of the plurality of layers of datacombinations corresponding to different processing algorithms anddifferent sample information. Because the plurality of layers of datacombinations uses different algorithms, a decision time length and asimulation depth may compromise with each other, and various gamescenarios are satisfied and requirements of different decision timelengths can be handled. Thus, the decision execution is simple andhighly efficient, and fast execution of a behavior is ensured.

When performing processing on each layer of data combination accordingto a preset processing algorithm to obtain a processing result of eachlayer of data combination, processing may be performed on the sampleinformation on each layer of data combination according to theprocessing algorithm corresponding to each layer of the plurality oflayers of data combinations, to obtain the processing result of eachlayer of data combination.

Optionally, in S404, the tagging the sample data according to aplurality of sample sequences of the sample data to obtain tagged sampledata includes: sequentially tagging each sample sequence according tothe priority by using a tagging frame, to obtain a plurality of taggedsample sequences; combining neighboring tagged sample sequences of theplurality of tagged sample sequences according to a same tagging frame,to obtain a combined tagged sample sequence; and tagging a start frameand an end frame of the combined tagged sample sequence, to obtain thetagged sample data.

FIG. 5 is a flowchart of a method of tagging sample data according to aplurality of sample sequences of the sample data according to anembodiment of the present disclosure. As shown in FIG. 5, the method oftagging sample data according to a plurality of sample sequences of thesample data includes the followings.

S501: Determining a priority of each of the plurality of samplesequences.

The sample data includes a plurality of sample sequences, and each ofthe plurality of sample sequences has a priority. Because of complexityof an execution rule of an event, a same data frame may belong to aplurality of sample sequences. After the sample data of event executionof the game client is obtained, the priority of each of the plurality ofsample sequences is determined, so that a ranking order of the pluralityof sample sequences is obtained according to the priority of each samplesequence.

S502: Sequentially tagging each sample sequence according to thepriority by using a tagging frame, to obtain a plurality of taggedsample sequences.

That is, each sample sequence is sequentially tagged according to thepriority by using a tagging frame, to obtain a plurality of taggedsample sequences. After the priority of each of the plurality of samplesequences is determined, different sample sequences may be tagged byusing tagging frames according to the ranking order of the plurality ofsample sequences. Optionally, the different sample sequences are taggedby using a preset rule or a preset sample segmentation algorithm and thetagging frames and according to the ranking order of the plurality ofsample sequences, to obtain the plurality of tagged sample sequences.

S503: Combining neighboring tagged sample sequences of the plurality oftagged sample sequences according to a same tagging frame, to obtain acombined tagged sample sequence.

After the plurality of tagged sample sequences is obtained bysequentially tagging each sample sequence according to the priority byusing a tagging frame, neighboring tagged sample sequences of theplurality of tagged sample sequences are combined according to a sametagging frame, to obtain a combined tagged sample sequence. Taggingframes of a plurality of tagged sample sequences may be traversed, andsample sequences of neighboring and same tagging frames are tagged as asame sequence, to obtain a combined tagged sample sequence.

S504: Tagging a start frame and an end frame of the combined taggedsample sequence, to obtain tagged sample data.

After the combined tagged sample sequence is obtained by combiningneighboring tagged sample sequences of the plurality of tagged samplesequences according to a same tagging frame, a start frame and an endframe of the combined tagged sample sequence may be tagged, to obtainthe tagged sample data.

According to one embodiment, a priority of each of the plurality ofsample sequences is determined; each sample sequence is sequentiallytagged according to the priority by using a tagging frame, to obtain aplurality of tagged sample sequences; neighboring tagged samplesequences of the plurality of tagged sample sequences are combinedaccording to a same tagging frame, to obtain a combined tagged samplesequence; and a start frame and an end frame of the combined taggedsample sequence are tagged, to obtain the tagged sample data. In thisway, the tagged sample data is obtained by tagging the sample dataaccording to a plurality of sample sequences of the sample data.

Optionally, in S404, the performing preprocessing on the tagged sampledata to obtain the plurality of layers of data combinations includes:assembling different state information of execution of a current eventobject of the game client, to obtain the plurality of layers of datacombinations.

FIG. 6 is a flowchart of a method of performing preprocessing on taggedsample data according to an embodiment of the present disclosure. Asshown in FIG. 6, the method of performing preprocessing on tagged sampledata includes the followings.

S601: Extracting, by using a preset state function, different stateinformation of execution of a current event object of the game clientfrom the tagged sample data.

After the tagged sample data is obtained by tagging the sample dataaccording to a plurality of sample sequences of the sample data,different state information on the current board is extracted from thetagged sample data by using a common state function. The different stateinformation may be used for indicating state information duringreal-time game playing of the game client, for example, an own-sidecharacter state, a friendly-side character state, and an adversary-sidecharacter state. The current event object is an event object currentlyexecuted by the game client.

S602: Assembling the different state information to obtain the pluralityof layers of data combinations.

After the different state information of execution of a current eventobject of the game client is extracted, by using a preset statefunction, from the tagged sample data, the different state informationis assembled, for example, state information such as an own-sidecharacter state, a friendly-side character state, and an adversary-sidecharacter state are assembled, to obtain the plurality of layers of datacombinations, each layer of the plurality of layers of data combinationscorresponding to a target event object in a same target event, differentlayers of the plurality of layers of data combinations corresponding todifferent target event objects in the target event, and the target eventobjects being event objects on the game client to be executedconcurrently.

For example, in a real-time game in which there are five persons in eachparty, own-side character states are placed on a first layer;friendly-side character states are ranked according to strength andplaced on a second layer to a fifth layer; adversary-side characterstates are ranked according to lethality and placed on a sixth layer toa tenth layer; map information and NPC information are placed on anoutermost layer. Other data assembling methods are also applicable.

According to one embodiment, different state information of execution ofa current event object of the game client is extracted, by using apreset state function, from the tagged sample data, the current eventobject being an event object currently executed by the game client; andthe different state information is assembled, to obtain the plurality oflayers of data combinations. In this way, the plurality of layers ofdata combinations is obtained by performing preprocessing on the taggedsample data, thereby improving data processing efficiency.

Optionally, before the performing processing on the sample informationon each layer of data combination according to the processing algorithmcorresponding to each layer of the plurality of layers of datacombinations, to obtain the processing result of each layer of datacombination, the sample information is generated according to the stateinformation, the character data on the game client and the event data ofthe target event.

FIG. 7 is a flowchart of another data processing method according to anembodiment of the present disclosure. As shown in FIG. 7, the dataprocessing method includes the followings.

S701: Obtaining character data on the game client.

There is character data when the game client executes the target event.The character data is used for representing data of a virtualapplication entity executing the target event. The character dataincludes a plurality of pieces of character data. Each piece ofcharacter data corresponds to data of a virtual application entity, andeach piece of character data on the game client is obtained.

S702: Mapping the state information and the character data to a presetprocessing model according to a preset mapping system, to obtain eventdata of a target event.

The state information and the character data may be mapped to alegitimate action space by using a game rule state. The action space hasevent data of the target event corresponding to the state informationand the character data. The event data may be action data, so that theevent data of the target event is obtained by using the stateinformation, the character data and the legitimate action space.

S703: Generating sample information according to the state information,the character data, and the event data.

After the event data of the target event is obtained by mapping thestate information and the character data to a preset processing modelaccording to a preset mapping system, the sample information isgenerated according to the state information, the character data and theevent data. The sample information includes frame information. Eachpiece of event data corresponds to one piece of sample information. Forexample, the state information, the character data and the event dataare represented by using <S, u, a>. S is used for representing the stateinformation, u is used for representing the character data, and a isused for representing the event data.

Thus, according to one embodiment, character data on the game client isobtained; the state information and the character data are mapped to apreset processing model according to a preset mapping system, to obtainevent data of the target event; the sample information is generatedaccording to the state information, the character data and the eventdata, and then processing is performed on the sample information on eachlayer of data combination according to the processing algorithmcorresponding to each layer of the plurality of layers of datacombinations, to obtain the processing result of each layer of datacombination. In this way, data processing efficiency is improved.

Optionally, after the sample information is generated according to thestate information, the character data and the event data, presetinformation is added to sample information of the extended samples.

FIG. 8 is a flowchart of another data processing method according to anembodiment of the present disclosure. As shown in FIG. 8, the dataprocessing method further includes the followings.

S801: Performing rotation processing on the sample data, to extend thesample quantity corresponding to the sample data.

After the sample information is generated according to the stateinformation, the character data and the event data, rotation processingis performed on the sample data, to extend the sample quantitycorresponding to the sample data to a predetermined number of sampledata.

S802: Adding preset information to the predetermined number of sampleinformation.

After the sample quantity corresponding to the sample data is extendedby performing rotation processing on the sample data, preset informationis added to sample information of the predetermined number of sampledata. The preset information may be other user information, for example,information such as an error rate or an operating frequency. Theinformation such as the error rate or the operating frequency is addedto the frame information, to facilitate training of a personalizedpolicy.

Optionally, in S406, the performing processing on the sample informationon each layer of data combination according to the processing algorithmcorresponding to each layer of the plurality of layers of datacombinations to obtain the processing result of each layer of datacombination includes: performing processing on the sample information oneach layer of data combination according to a preset probability modeland a preset value model corresponding to each layer of datacombination, to obtain an execution probability and an execution valuethat are of execution of the target event of the game client and thatcorrespond to each layer of data combination, and obtaining a targetinstruction according to the execution probability corresponding to eachlayer of data combination and the execution value corresponding to eachlayer of data combination.

FIG. 9 is a flowchart of a method of performing processing on sampleinformation on each layer of data combination according to a processingalgorithm corresponding to each layer of a plurality of layers of datacombinations according to an embodiment of the present disclosure. Asshown in FIG. 9, the method includes the followings.

S901: Performing processing on the sample information on each layer ofdata combination according to a preset probability model correspondingto each layer of data combination, to obtain an execution probabilitythat is of execution of the target event of the game client and thatcorresponds to each layer of data combination.

In the technical solution provided in S901 in this application, eachlayer of data combination corresponds to a preset probability model, andeach layer of data combination learns, according to the sampleinformation of each layer of data combination, a preset probabilitymodel of action execution in the current state, to obtain an executionprobability that is of execution of the target event of the game clientand that corresponds to each layer of data combination.

S902: Performing processing on the sample information on each layer ofdata combination according to a preset value model corresponding to eachlayer of data combination, to obtain an execution value that is ofexecution of the target event of the game client and that corresponds toeach layer of data combination.

That is, each layer of data combination corresponds to a preset valuemodel, and each layer of data combination learns, according to thesample information of each layer of data combination, a preset valuemodel of action execution in the current state, to obtain a valueprobability that is of execution of the target event of the game clientand that corresponds to each layer of data combination.

S903: Performing consolidation processing on the execution probabilitycorresponding to each layer of data combination and the execution valuecorresponding to each layer of data combination, to obtain the targetinstruction.

After the execution probability that is of execution of the target eventof the game client and that corresponds to each layer of datacombination and the execution value that is of execution of the targetevent of the game client and that corresponds to each layer of datacombination are obtained, consolidation processing is performed on theexecution probability corresponding to each layer of data combinationand the execution value corresponding to each layer of data combination,to obtain the target instruction. A final policy is outputted. A stateevaluation function is added during execution of the policy. In thisway, whether the policy on the current board needs to be changed isdetermined and various event execution environments are handled.

Thus, according to one embodiment, processing is performed on the sampleinformation on each layer of data combination according to a presetprobability model corresponding to each layer of data combination, toobtain an execution probability that is of execution of the target eventof the game client and that corresponds to each layer of datacombination; and processing is performed on the sample information oneach layer of data combination according to a preset value modelcorresponding to each layer of data combination, to obtain an executionvalue that is of execution of the target event of the game client andthat corresponds to each layer of data combination. In this way, theprocessing result of each layer of data combination is obtained byperforming processing on the sample information on each layer of datacombination according to the processing algorithm corresponding to eachlayer of the plurality of layers of data combinations, and the targetinstruction is obtained by performing consolidation processing on theexecution probability corresponding to each layer of data combinationand the execution value corresponding to each layer of data combination,thereby improving data processing efficiency.

Optionally, after the target instruction is obtained by performingconsolidation processing on the processing result of each layer of datacombination, the target instruction is updated if the target instructionneeds to be updated.

FIG. 10 is a flowchart of another data processing method according to anembodiment of the present disclosure. As shown in FIG. 10, the dataprocessing method includes the followings.

S1001: Determining, according to a preset state evaluation function,whether the target instruction needs to be updated.

After the target instruction is obtained by obtaining a processingresult of each layer of data combination and performing consolidationprocessing on the processing result of each layer of data combination,the target event is executed according to the target instruction, andcorresponding game state information is returned. Whether the targetinstruction needs to be updated is determined according to a presetstate evaluation function, and whether the target instruction needs tobe updated may be determined by using a behavior tree.

S1002: Updating the target instruction if it is determined that thetarget instruction needs to be updated.

After it is determined, according to a preset state evaluation function,whether the target instruction needs to be updated, the targetinstruction is updated if it is determined that the target instructionneeds to be updated, thereby handling various event processingenvironments.

Thus, according to one embodiment, after the target instruction isobtained by performing consolidation processing on the processing resultof each layer of data combination, whether the target instruction needsto be updated is determined according to a preset state evaluationfunction, and the target instruction is updated if it is determined thatthe target instruction needs to be updated, thereby improving dataprocessing efficiency.

Optionally, after the target instruction is obtained by performingconsolidation processing on the processing result of each layer of datacombination, the processing result of each layer of data combination isupdated according to different target state information during executionof the different target event objects according to the targetinstruction, to obtain an updated processing result of each layer ofdata combination, and consolidation processing is performed on theupdated processing results of the plurality of layers of datacombinations, to obtain an updated target instruction.

FIG. 11 is a flowchart of another data processing method according to anembodiment of the present disclosure. As shown in FIG. 11, the dataprocessing method includes the followings.

S1101: Obtaining different target state information during execution ofdifferent target event objects according to the target instruction bythe game client.

After the target instruction is obtained by obtaining a processingresult of each layer of data combination and performing consolidationprocessing on the processing result of each layer of data combination,the game client executes different target event objects according to thetarget instruction and obtains different target state information duringexecution of the different target event objects according to the targetinstruction by the game client.

S1102: Updating the processing result of each layer of data combinationaccording to the different target state information, to obtain anupdated processing result of each layer of data combination.

After the different target state information during execution of thedifferent target event objects according to the target instruction bythe game client is obtained, the processing result of each layer of datacombination is updated according to the different target stateinformation, to obtain an updated processing result of each layer ofdata combination. After the updated processing result of each layer ofdata combination is obtained, consolidation processing is performed onthe updated processing result s of the plurality of layers of datacombinations, to obtain an updated target instruction.

Thus, according to one embodiment, the different target stateinformation during execution of the different target event objectsaccording to the target instruction by the game client is obtained, andthe processing result of each layer of data combination is updatedaccording to the different target state information, to obtain anupdated processing result of each layer of data combination, therebyimproving data processing efficiency.

The following describes the technical solutions in the presentdisclosure with reference to an embodiment. Specifically, a gameintelligent system is used as an example for description.

A real-time game is generally characterized as having complex gamerules, various dynamic scenarios, uncertain behaviors, incompleteinformation, short decision time, a success probability, and the like.Considering such a large decision space and a requirement ofreal-timeliness of decision, how to set, select, and execute a policy isa primary problem faced by the game intelligent system. In a turn-basedgame, the method of using a plurality of deep learning networks isproved to have a relatively strong decision capability. However, thismethod cannot be directly applied to a real-time game. Deep learning isa neural network algorithm using a plurality of complex structures ornon-linear transformation processing layers, and has a high-levelabstraction capability better than that of a shallow neural network. Thereal-time game is a type of game in which a game process is performedinstantly instead of turn-based, such as the game of Go or chess.

According to one embodiment, the policy selection is dispersed to aplurality of layers, so that a large number of flattened data can belearned in a dispersed manner. In this way, the dimension of the statespace is reduced, and different algorithms may be used on differentlayers. Accordingly, the decision time length and the simulation depthmay compromise with each other, so that various game scenarios aresatisfied.

According to one embodiment, a decision process of a human player issimulated, and an entire intelligent system is divided into threemodules: a decision selection module, a decision execution module and afeedback optimization module, so that the system can handle variouscomplex scenarios in a real-time game. Macroscopically, considering theproblem of decision depth of a game player, decision learning isperformed by selecting a proper data sample and algorithm according todifferent levels of abstraction downward, thereby reducing complexity ofan operation. Microcosmically, decision is executed by using fast andsimple algorithms, and the result can be fed back, avoiding excessivelymuch consideration to decision. Such method may be applied to ahuman-computer battle mode in a real-time game and may provide morepersonified artificial-intelligence characters, thereby improvingexperience of a player.

FIG. 12 is a schematic diagram of an interaction process in a gameprocess according to an embodiment of the present disclosure. As shownin FIG. 12, in one embodiment, policy selection and policy execution areseparated. The decision layer includes a policy layer 1, a policy layer2, . . . , a policy layer n, thereby having a relatively big depth andcapable of simulating a game decision path of a player. The policyexecution module focuses on execution efficiency without makingexcessively many decisions, and performs optimization on feedbacks.

Because there is a large number of design dimensions of game data, thegame data cannot be directly used as training data and needs to betagged according to a preset rule. A main method is to tag an entiresample according to a characteristic sequence of the input sample and apreset logical configuration. FIG. 13 is a flowchart of another methodof tagging sample data according to a plurality of sample sequences ofthe sample data according to an embodiment of the present disclosure. Asshown in FIG. 13, the method of tagging sample data according to aplurality of sample sequences of the sample data includes thefollowings.

S1301: Determining priorities of sample sequences according tocharacteristics of a game, to obtain a sample sequence order.

Due to complexity of a game rule, a same frame may belong to varioussequences. Therefore, priorities of sample sequences need to bedetermined according to characteristics of a game at the beginning.

S1302: Tagging different sequences according to the sample sequenceorder.

After the sample sequence order is obtained by determining priorities ofsample sequences according to characteristics of a game, differentsequences are tagged according to the sample sequence order. Thedifferent sequences may be tagged according to the sample sequence orderby using a preset rule or a sample segmentation algorithm.

S1303: Traversing all tagging frames.

After the different sequences are tagged according to the samplesequence order, all tagging frames are traversed, and a first sequencebackward having a tagging frame is tagged.

S1304: Tagging neighboring and same frames as a same sequence.

During traversing of all frames, neighboring and same frames are taggedas a same sequence.

S1305: Tagging a start frame and an end frame of each sequence.

After the neighboring and same frames are tagged as a same sequence, astart frame and an end frame of each sequence are tagged.

According to one embodiment, priorities of sample sequences aredetermined according to characteristics of a game, to obtain a samplesequence order; different sequences are tagged according to the samplesequence order; all tagging frames are traversed; neighboring and sameframes are tagged as a same sequence; and a start frame and an end frameof each sequence are tagged. In this way, the sample data is taggedaccording to a plurality of sample sequences of the sample data.

FIG. 14 is a flowchart of another data processing method according to anembodiment of the present disclosure. As shown in FIG. 14, the method ofperforming preprocessing on tagged sample data includes the followings.

S1401: Extracting state information on a current game state from asample by using a common state function.

That is, the state information on a current game state extracted from asample by using a common state function is referred to as an S state.

S1402: Assembling the state information to form a plurality of layers ofdata combinations.

After the state information on the current game state is extracted fromthe sample by using the common state function, the state information isassembled to form the plurality of layers of data combinations. It isassumed that in a real-time game in which there are five persons in eachparty, own-side character states are placed on a first layer;friendly-side character states are ranked according to strength andplaced on a second layer to a fifth layer; adversary-side characters areranked according to lethality and placed on a sixth layer to a tenthlayer; map information and NPC information are placed on an outermostlayer. Other data assembling principles are also applicable.

S1403: Mapping the state information of the sample on each layer of datacombination and each piece of character data to a legitimate actionspace according to a game rule state, to obtain event data.

After the state information is assembled to form the plurality of layersof data combinations, the state information of the sample on each layerof data combination and each piece of character data u are mapped to alegitimate action space according to a game rule state, to obtain eventdata a.

S1404: Generating <S, u, a> according to each action sample, and rotatethe samples to extend the sample quantity.

After the event data a is obtained, <S, u, a> is generated according tothe state information S, the character data u and the event data a ofeach action sample, and the samples are rotated to extend apredetermined number of samples.

S1405: Adding preset information to sample information of the extendedpredetermined number of sample data.

Other user information such as an error rate is added. An operatingfrequency may also be added to frame information, to facilitate trainingof personalized AI.

An execution probability model of an action and a value model of theaction in the current state are learned according to the <S, u, a>information on each decision layer. For an algorithm, refer to AlphaGoin which a policy network and a value network are consolidated by usingan MCTS algorithm. Output on each decision layer is weighted andconsolidated, so that a final policy is obtained. A state evaluationfunction may be added to the policy, to determine whether the policy onthe current game state needs to be changed, so as to satisfy variousgame environments.

During policy execution, corresponding game state information isreturned, so that a policy selection module performs updating andlearning. The algorithm may be a behavior tree.

One embodiment provides a multi-layer intelligent system architecture,that is, a construction concept of dividing an intelligent system into aplurality of decision layers, to simulate a multi-layer abstractdecision behavior of a player in an actual game. Decision selection anddecision execution are separated to handle requirements of a real-timegame. A multi-layer deep learning framework is used in the decisionlayers, to properly construct samples and tag and process sample policysequences. In addition, requirements of different decision time lengthsmay be handled, the decision execution is simple and highly efficient,and fast execution of a behavior is ensured, thereby improving dataprocessing efficiency. The entire system simulates a thinking process ofa player, so that capability of AI can be effectively improved, therebyimproving user experience of a game player.

The technical solutions in the present disclosure may be applied to ahuman-computer battle in a real-time game and may provide morepersonified artificial-intelligence characters, thereby improvingexperience of a player. FIG. 15 is a schematic flowchart of a gameinteraction method according to an embodiment of the present disclosure.

As shown in FIG. 15, a game client obtains a current game state, andsends the current game state to a policy selection server by using anetwork. The policy selection server includes a plurality of servers,performs policy selection by using a model and selects an optimal actionand returns the action to the game client. The game client executes apolicy according to the optimal action and feeds back game stateinformation and the policy.

According to one embodiment, a decision process of a human player issimulated, and an entire intelligent system is divided into threemodules: a decision selection module, a decision execution module and afeedback optimization module, so that the system can handle variouscomplex scenarios in a real-time game. Macroscopically, the problem ofdecision depth of a game player is considered, and decision learning isperformed by selecting a proper data sample and algorithm according todifferent levels of abstraction downward, thereby reducing complexity ofan operation. Microcosmically, decision is executed by using fast andsimple algorithms and the result can be fed back, avoiding excessivelymuch consideration to decision. By using the game interaction method,policy selection and policy execution are divided, so that the decisionlayer has a relatively big depth and a game decision path of a playercan be simulated. The execution layer focuses on execution efficiencywithout making excessively many decisions, thereby improving dataprocessing efficiency.

It should be noted that for each of the foregoing method embodiments,for ease of description, the method embodiment is described as a seriesof action combinations. However, persons skilled in the art should knowthat the present disclosure is not limited to the described order ofactions, because according to the present disclosure, some steps may beperformed in another order or be performed concurrently. In addition,persons skilled in the art should also know that all of the embodimentsdescribed in this specification are preferred embodiments, and therelated actions and modules are not necessarily required in the presentdisclosure.

According to the description of the foregoing implementation, personsskilled in the art may clearly learn that the method in the foregoingembodiment may be implemented by relying on software and a necessarycommon hardware platform or by using hardware, but the former one is apreferred implementation in many cases. Based on such an understanding,the technical solutions in the present disclosure essentially, or thepart contributing to the existing technology may be implemented in theform of a software product. The computer software product is stored in astorage medium (for example, a read-only memory (ROM)/random accessmemory (RAM), a magnetic disk, or an optical disc) and includes severalinstructions for instructing a terminal device (which may be a mobilephone, a computer, a server, a network device, or the like) to performthe method described in the embodiments of the present disclosure.

According to an embodiment of the present disclosure, a data processingapparatus for performing the foregoing data processing method is furtherprovided. FIG. 16 is a schematic diagram of a data processing apparatusaccording to an embodiment of the present disclosure. As shown in FIG.16, the data processing apparatus may include: a first obtaining unit10, a first processing unit 20, a second processing unit 30 and a thirdprocessing unit 40.

The first obtaining unit 10 is configured to obtain sample data of eventexecution of a game client.

The first processing unit 20 is configured to perform preprocessing onthe sample data to obtain a plurality of layers of data combinations,each layer of the plurality of layers of data combinations correspondingto a target event object in a same target event, different layers of theplurality of layers of data combinations corresponding to differenttarget event objects in the target event, and the target event objectsbeing event objects on the game client to be executed concurrently.

The second processing unit 30 is configured to perform processing oneach layer of data combination according to a preset processingalgorithm, to obtain a processing result of each layer of datacombination.

The third processing unit 40 is configured to perform consolidationprocessing on the processing result of each layer of data combination toobtain a target instruction, the target instruction being used forinstructing the game client to concurrently execute the different targetevent objects corresponding to the different layers of datacombinations.

It should be noted herein that, the first obtaining unit 10, the firstprocessing unit 20, the second processing unit 30 and the thirdprocessing unit 40 may be used as a part of the apparatus and run in aterminal. A processor in the terminal may be used for performingfunctions implemented by the foregoing modules. The terminal may be aterminal device such as a smartphone (for example, an Android mobilephone or an iOS mobile phone), a tablet computer, a palmtop computer, amobile Internet device (MID), or a PAD.

It should be noted that, the first obtaining unit 10 in one embodimentmay be configured to perform S402, the first processing unit 20 in oneembodiment may be configured to perform S404, the second processing unit30 in one embodiment may be configured to perform S406, and the thirdprocessing unit 40 in one embodiment may be configured to perform S408,etc.

FIG. 17 is a schematic diagram of another data processing apparatusaccording to an embodiment of the present disclosure. As shown in FIG.17, the data processing apparatus may include: a first obtaining unit10, a first processing unit 20, a second processing unit 30 and a thirdprocessing unit 40. The first processing unit 20 includes: a taggingmodule 21 and a processing module 22.

It should be noted that, the first obtaining unit 10, the firstprocessing unit 20, the second processing unit 30 and the thirdprocessing unit 40 in one embodiment play the same role as those in thedata processing apparatus in the embodiment shown in FIG. 16, anddetails are not described herein again.

The tagging module 21 is configured to tag the sample data according toa plurality of sample sequences of the sample data, to obtain taggedsample data.

The processing module 22 is configured to perform preprocessing on thetagged sample data to obtain the plurality of layers of datacombinations, different layers of the plurality of layers of datacombinations corresponding to different processing algorithms anddifferent sample information.

The second processing unit 30 is configured to perform processing on thesample information on each layer of data combination according to theprocessing algorithm corresponding to each layer of the plurality oflayers of data combinations, to obtain the processing result of eachlayer of data combination.

It should be noted herein that, the tagging module 21 and the processingmodule 22 may be used as a part of the apparatus and run in a terminal.A processor in the terminal may be used for performing functionsimplemented by the foregoing modules. The terminal may be a terminaldevice such as a smartphone (for example, an Android mobile phone or aniOS mobile phone), a tablet computer, a palmtop computer, an MID, or aPAD.

FIG. 18 is a schematic diagram of another data processing apparatusaccording to an embodiment of the present disclosure. As shown in FIG.18, the data processing apparatus may include: a first obtaining unit10, a first processing unit 20, a second processing unit 30 and a thirdprocessing unit 40. The first processing unit 20 includes: a taggingmodule 21 and a processing module 22. The tagging module 21 includes: adetermining submodule 211, a first tagging submodule 212, a combinationsubmodule 213 and a second tagging submodule 214.

It should be noted that, the first obtaining unit 10, the firstprocessing unit 20, the second processing unit 30, the third processingunit 40, the tagging module 21, and the processing module 22 in oneembodiment play the same role as those in the data processing apparatusin the embodiment shown in FIG. 17, and details are not described hereinagain.

The determining submodule 211 is configured to determine a priority ofeach of the plurality of sample sequences.

The first tagging submodule 212 is configured to sequentially tag eachsample sequence according to the priority by using a tagging frame, toobtain a plurality of tagged sample sequences.

The combination submodule 213 is configured to combine neighboringtagged sample sequences of the plurality of tagged sample sequencesaccording to a same tagging frame, to obtain a combined tagged samplesequence.

The second tagging submodule 214 is configured to tag a start frame andan end frame of the combined tagged sample sequence, to obtain thetagged sample data.

It should be noted herein that, the determining submodule 211, the firsttagging submodule 212, the combination submodule 213, and the secondtagging submodule 214 may be used as a part of the apparatus and run ina terminal. A processor in the terminal may be used for performingfunctions implemented by the foregoing modules. The terminal may be aterminal device such as a smartphone (for example, an Android mobilephone or an iOS mobile phone), a tablet computer, a palmtop computer, anMID, or a PAD.

FIG. 19 is a schematic diagram of another data processing apparatusaccording to an embodiment of the present disclosure. As shown in FIG.19, the data processing apparatus may include: a first obtaining unit10, a first processing unit 20, a second processing unit 30 and a thirdprocessing unit 40. The first processing unit 20 includes: a taggingmodule 21 and a processing module 22. The processing module 22 includes:an extraction submodule 221 and an assembly submodule 222.

It should be noted that, the first obtaining unit 10, the firstprocessing unit 20, the second processing unit 30, the third processingunit 40, the tagging module 21, and the processing module 22 in oneembodiment play the same role as those in the data processing apparatusin the embodiment shown in FIG. 17.

The extraction submodule 221 is configured to extract, by using a presetstate function, different state information of execution of a currentevent object of the game client from the tagged sample data, the currentevent object being an event object currently executed by the gameclient.

The assembly submodule 222 is configured to assemble the different stateinformation, to obtain the plurality of layers of data combinations.

It should be noted herein that, the extraction submodule 221 and theassembly submodule 222 may be used as a part of the apparatus and run ina terminal. A processor in the terminal may be used for performingfunctions implemented by the foregoing modules. The terminal may be aterminal device such as a smartphone (for example, an Android mobilephone or an iOS mobile phone), a tablet computer, a palmtop computer, anMID, or a PAD.

FIG. 20 is a schematic diagram of another data processing apparatusaccording to an embodiment of the present disclosure. As shown in FIG.20, the data processing apparatus may include: a first obtaining unit10, a first processing unit 20, a second processing unit 30 and a thirdprocessing unit 40. The first processing unit 20 includes: a taggingmodule 21 and a processing module 22. The processing module 22 includes:an extraction submodule 221 and an assembly submodule 222. The dataprocessing apparatus further includes: a second obtaining unit 50, amapping unit 60 and a generation unit 70.

It should be noted that, the first obtaining unit 10, the firstprocessing unit 20, the second processing unit 30, the third processingunit 40, the extraction submodule 221 and the assembly submodule 222 inone embodiment play the same role as those in the data processingapparatus in the embodiment shown in FIG. 19, and details are notdescribed herein again.

The second obtaining unit 50 is configured to: before the performingprocessing on the sample information on each layer of data combinationaccording to the processing algorithm corresponding to each layer of theplurality of layers of data combinations, to obtain the processingresult of each layer of data combination, obtain character data on thegame client.

The mapping unit 60 is configured to map the state information and thecharacter data to a preset processing model according to a presetmapping system, to obtain event data of the target event.

The generation unit 70 is configured to generate the sample informationaccording to the state information, the character data and the eventdata.

It should be noted herein that, the second obtaining unit 50, themapping unit 60, and the generation unit 70 may be used as a part of theapparatus and run in a terminal. A processor in the terminal may be usedfor performing functions implemented by the foregoing modules. Theterminal may be a terminal device such as a smartphone (for example, anAndroid mobile phone or an iOS mobile phone), a tablet computer, apalmtop computer, an MID, or a PAD.

FIG. 21 is a schematic diagram of another data processing apparatusaccording to an embodiment of the present disclosure. As shown in FIG.21, the data processing apparatus may include: a first obtaining unit10, a first processing unit 20, a second processing unit 30, a thirdprocessing unit 40, a second obtaining unit 50, a mapping unit 60 and ageneration unit 70. The first processing unit 20 includes: a taggingmodule 21 and a processing module 22. The processing module 22 includes:an extraction submodule 221 and an assembly submodule 222. The dataprocessing apparatus further includes: a fourth processing unit 80 andan adding unit 90.

It should be noted that, the first obtaining unit 10, the firstprocessing unit 20, the second processing unit 30, the third processingunit 40, the second obtaining unit 50, the mapping unit 60 and thegeneration unit 70, the tagging module 21 and the processing module 22,and the extraction submodule 221 and the assembly submodule 222 in oneembodiment play the same role as those in the data processing apparatusin the embodiment shown in FIG. 20, and details are not described hereinagain.

The fourth processing unit 80 is configured to: after the generating thesample information according to the state information, the characterdata and the event data, perform rotation processing on the sample data,to extend the sample quantity corresponding to the sample data.

The adding unit 90 is configured to add preset information to sampleinformation of the extended sample data.

It should be noted herein that, the fourth processing unit 80 and theadding unit 90 may be used as a part of the apparatus and run in aterminal. A processor in the terminal may be used for performingfunctions implemented by the foregoing modules. The terminal may be aterminal device such as a smartphone (for example, an Android mobilephone or an iOS mobile phone), a tablet computer, a palmtop computer, anMID, or a PAD.

FIG. 22 is a schematic diagram of another data processing apparatusaccording to an embodiment of the present disclosure. As shown in FIG.22, the data processing apparatus may include: a first obtaining unit10, a first processing unit 20, a second processing unit 30 and a thirdprocessing unit 40. The second processing unit 30 includes: a firstprocessing module 31 and a second processing module 32.

It should be noted that, the first obtaining unit 10, the firstprocessing unit 20, the second processing unit 30 and the thirdprocessing unit 40 in one embodiment play the same role as those in thedata processing apparatus in the embodiment shown in FIG. 16, anddetails are not described herein again.

The first processing module 31 is configured to perform processing onthe sample information on each layer of data combination according to apreset probability model corresponding to each layer of datacombination, to obtain an execution probability that is of execution ofthe target event of the game client and that corresponds to each layerof data combination.

The second processing module 32 is configured to perform processing onthe sample information on each layer of data combination according to apreset value model corresponding to each layer of data combination, toobtain an execution value that is of execution of the target event ofthe game client and that corresponds to each layer of data combination.

The third processing unit 40 is configured to perform consolidationprocessing on the execution probability corresponding to each layer ofdata combination and the execution value corresponding to each layer ofdata combination, to obtain the target instruction.

It should be noted herein that, the first processing module 31 and thesecond processing module 32 may be used as a part of the apparatus andrun in a terminal. A processor in the terminal may be used forperforming functions implemented by the foregoing modules.

The terminal may be a terminal device such as a smartphone (for example,an Android mobile phone or an iOS mobile phone), a tablet computer, apalmtop computer, an MID, or a PAD.

FIG. 23 is a schematic diagram of another data processing apparatusaccording to an embodiment of the present disclosure. As shown in FIG.23, the data processing apparatus may include: a first obtaining unit10, a first processing unit 20, a second processing unit 30, a secondprocessing unit 30 and a third processing unit 40. The data processingapparatus further includes: a determining unit 100 and an update unit110.

It should be noted that, the first obtaining unit 10, the firstprocessing unit 20, the second processing unit 30 and the thirdprocessing unit 40 in one embodiment play the same role as those in thedata processing apparatus in the embodiment shown in FIG. 16, anddetails are not described herein again.

The determining unit 100 is configured to: after the obtaining aprocessing result of each layer of data combination and performingconsolidation processing on the processing result of each layer of datacombination to obtain a target instruction, determine, according to apreset state evaluation function, whether the target instruction needsto be updated.

The update unit 110 is configured to update the target instruction whenit is determined that the target instruction needs to be updated.

It should be noted herein that, the determining unit 100 and the updateunit 110 may be used as a part of the apparatus and run in a terminal. Aprocessor in the terminal may be used for performing functionsimplemented by the foregoing modules. The terminal may be a terminaldevice such as a smartphone (for example, an Android mobile phone or aniOS mobile phone), a tablet computer, a palmtop computer, an MID, or aPAD.

According to one embodiment of the present disclosure, the firstobtaining unit 10 obtains sample data of event execution of a gameclient; the first processing unit 20 preprocesses the sample data toobtain a plurality of layers of data combinations, each layer of theplurality of layers of data combinations corresponding to a target eventobject in a same target event, different layers of the plurality oflayers of data combinations corresponding to different target eventobjects in the target event, and the target event objects being eventobjects on the game client to be executed concurrently; the secondprocessing unit 30 processes each layer of data combination according toa preset processing algorithm, to obtain a processing result of eachlayer of data combination; and the third processing unit 40 performsconsolidation processing on the processing result of each layer of datacombination to obtain a target instruction, the target instruction beingused for instructing the game client to concurrently execute thedifferent target event objects corresponding to the different layers ofdata combinations. In this way, a technical problem of a low dataprocessing efficiency in the related art is resolved, and a technicaleffect of improving data processing efficiency is achieved.

It should be noted herein that, examples and application scenarios ofthe foregoing units and modules are the same as those implemented in thecorresponding steps, but are not limited to the content disclosed in theforegoing embodiments. It should be noted that, the modules as a part ofthe apparatus may run in the hardware environment shown in FIG. 3, andmay be implemented by software, or may be implemented by hardware. Thehardware environment includes a network environment.

The functional modules provided in the embodiments in this applicationmay run in a mobile terminal, a computer terminal or a similar operationapparatus, or may be used as a part of a storage medium for storage.

An embodiment of the present disclosure may provide a terminal. Theterminal may be any computer terminal device in a computer terminalgroup. Optionally, in one embodiment, the terminal may be replaced by aterminal device such as a mobile terminal.

Optionally, in one embodiment, the foregoing terminal may be located inat least one of a plurality of network devices in a computer network.

According to an embodiment of the present disclosure, a terminal forperforming the foregoing data processing method is further provided. Theterminal may be a computer terminal. The computer terminal may be anycomputer terminal device in a computer terminal group. Optionally, inone embodiment, the computer terminal may be replaced by a terminaldevice such as a mobile terminal.

Optionally, in one embodiment, the foregoing computer terminal may belocated in at least one of a plurality of network devices in a computernetwork.

FIG. 24 is a structural block diagram of a terminal according to anembodiment of the present disclosure. As shown in FIG. 24, the terminalmay include: one or more (only one is shown in the figure) processors241, a memory 243, and a transmission apparatus 245. As shown in FIG.24, the terminal may further include an input output device 247.

The memory 243 may be configured to store a software program and module,for example, program instructions/modules corresponding to the dataprocessing method and apparatus in the embodiments of the presentdisclosure. The processor 241 runs the software program and modulestored in the memory 243 to implement various function application anddata processing, that is, implement the foregoing data processingmethod. The memory 243 may include a high-speed random memory, and mayfurther include a non-volatile memory such as one or more magneticstorage apparatuses, a flash, or another non-volatile solid-statememory. In some examples, the memory 243 may further include memoriesremotely disposed relative to the processor 241, and the remote memoriesmay be connected to the terminal by using a network. Examples of thenetwork include but are not limited to the Internet, an intranet, alocal area network, a mobile communications network, and a combinationthereof.

The foregoing transmission apparatus 245 is configured to receive orsend data by using a network, and may further be configured to performprocessing on data transmission between the processor and the memory.Specific examples of the foregoing network may include a wired networkand a wireless network. In an example, the transmission apparatus 245includes a network adapter (network interface controller, NIC). Thenetwork adapter may be connected to another network device and a routerby using a network cable, so as to perform communication with theInternet or a local network. In an example, the transmission apparatus245 is a radio frequency (RF) module, and is configured to communicatewith the Internet in a wireless manner.

Specifically, the memory 243 is configured to store an applicationprogram.

The processor 241 may invoke, by using the transmission apparatus 245,the application program stored in the memory 243, to execute programcode of steps of a method of each optional or preferred embodiment inthe foregoing method embodiments, including: obtaining sample data ofevent execution of a game client; performing preprocessing on the sampledata to obtain a plurality of layers of data combinations, each layer ofthe plurality of layers of data combinations corresponding to a targetevent object in a same target event, different layers of the pluralityof layers of data combinations corresponding to different target eventobjects in the target event, and the target event objects being eventobjects on the game client to be executed concurrently; processingsample information on each layer of data combination according to apreset processing algorithm, to obtain a processing result of each layerof data combination; and performing consolidation processing on theprocessing result of each layer of data combination to obtain a targetinstruction, the target instruction being used for instructing the gameclient to concurrently execute the different target event objectscorresponding to the different layers of data combinations.

The processor 241 is further configured to perform the followings:tagging the sample data according to a plurality of sample sequences ofthe sample data, to obtain tagged sample data; performing preprocessingon the tagged sample data to obtain the plurality of layers of datacombinations, different layers of the plurality of layers of datacombinations corresponding to different processing algorithms anddifferent sample information; and performing processing on the sampleinformation on each layer of data combination according to theprocessing algorithm corresponding to each layer of the plurality oflayers of data combinations, to obtain the processing result of eachlayer of data combination.

The processor 241 is further configured to perform the followings:determining a priority of each of the plurality of sample sequences;sequentially tagging each sample sequence according to the priority byusing a tagging frame, to obtain a plurality of tagged sample sequences;combining neighboring tagged sample sequences of the plurality of taggedsample sequences according to a same tagging frame, to obtain a combinedtagged sample sequence; and tagging a start frame and an end frame ofthe combined tagged sample sequence, to obtain the tagged sample data.

The processor 241 is further configured to perform the followings:extracting, by using a preset state function, different stateinformation of execution of a current event object of the game clientfrom the tagged sample data, the current event object being an eventobject currently executed by the game client; and assembling thedifferent state information, to obtain the plurality of layers of datacombinations.

The processor 241 is further configured to perform the followings:before the performing processing on the sample information on each layerof data combination according to the processing algorithm correspondingto each layer of the plurality of layers of data combinations, to obtainthe processing result of each layer of data combination, obtainingcharacter data on the game client; mapping the state information and thecharacter data to a preset processing model according to a presetmapping system, to obtain event data of the target event; and generatingthe sample information according to the state information, the characterdata and the event data.

The processor 241 is further configured to perform the followings: afterthe generating the sample information according to the stateinformation, the character data and the event data, performing rotationprocessing on the sample data, to extend the sample quantitycorresponding to the sample data; and adding preset information tosample information of the extended sample data.

The processor 241 is further configured to perform the followings:performing processing on the sample information on each layer of datacombination according to a preset probability model corresponding toeach layer of data combination, to obtain an execution probability thatis of execution of the target event of the game client and thatcorresponds to each layer of data combination; and performing processingon the sample information on each layer of data combination according toa preset value model corresponding to each layer of data combination, toobtain an execution value that is of execution of the target event ofthe game client and that corresponds to each layer of data combination,the obtaining a processing result of each layer of data combination andperforming consolidation processing on the processing result of eachlayer of data combination to obtain a target instruction including:performing consolidation processing on the execution probabilitycorresponding to each layer of data combination and the execution valuecorresponding to each layer of data combination, to obtain the targetinstruction.

The processor 241 is further configured to perform the followings: afterthe obtaining a processing result of each layer of data combination andperforming consolidation processing on the processing result of eachlayer of data combination to obtain a target instruction, determining,according to a preset state evaluation function, whether the targetinstruction needs to be updated; and updating the target instruction ifit is determined that the target instruction needs to be updated.

The processor 241 is further configured to perform the followings: afterthe obtaining a processing result of each layer of data combination andperforming consolidation processing on the processing result of eachlayer of data combination to obtain a target instruction, obtainingdifferent target state information during execution of the differenttarget event objects according to the target instruction by the gameclient; and updating the processing result of each layer of datacombination according to the different target state information, toobtain an updated processing result of each layer of data combination,the obtaining a processing result of each layer of data combination andperforming consolidation processing on the processing result of eachlayer of data combination to obtain a target instruction including:obtaining the updated processing result of each layer of datacombination, and performing consolidation processing on the updatedprocessing results of the plurality of layers of data combinations, toobtain an updated target instruction.

According to one embodiment of the present disclosure, a solution of adata processing method is provided. Sample data of event execution of agame client is obtained; the sample data is tagged according to aplurality of sample sequences of the sample data, to obtain taggedsample data; preprocessing is performed on the tagged sample data toobtain a plurality of layers of data combinations, each layer of theplurality of layers of data combinations corresponding to a target eventobject in a same target event, different layers of the plurality oflayers of data combinations corresponding to different target eventobjects in the target event, the target event objects being eventobjects on the game client to be executed concurrently, and differentlayers of data combinations corresponding to different processingalgorithms and different sample information; processing is performed onthe sample information on each layer of data combination according tothe processing algorithm corresponding to each layer of the plurality oflayers of data combinations, to obtain a processing result of each layerof data combination; and the processing result of each layer of datacombination is obtained and consolidation processing is performed on theprocessing result of each layer of data combination to obtain a targetinstruction, the target instruction being used for instructing the gameclient to concurrently execute the different target event objectscorresponding to the different layers of data combinations. In this way,an objective of obtaining a target instruction by performingconsolidation processing on the processing result of each layer of theplurality of layers of data combinations is achieved, and a technicaleffect of improving data processing efficiency is achieved, therebyresolving a technical problem of a low data processing efficiency in therelated art. For a specific example, refer to the examples described inthe foregoing embodiments.

Persons of ordinary skill in the art may understand that the structureshown in FIG. 24 is only exemplary. The terminal may be a terminaldevice such as a smartphone (for example, an Android mobile phone or aniOS mobile phone), a tablet computer, a palmtop computer, an MID, or aPAD. FIG. 24 does not limit the structure of the foregoing electronicdevice. For example, the terminal may include more or fewer components(such as a network interface or a display apparatus) than that are shownin FIG. 24, or may have a configuration different from that shown inFIG. 24.

Persons of ordinary skill in the art may understand that all or some ofthe steps of the methods in the foregoing embodiments may be implementedby a program instructing relevant hardware. The program may be stored ina computer-readable storage medium, and the storage medium may be aflash memory, a ROM, a RAM, a magnetic disk, an optical disc, or thelike.

An embodiment of the present disclosure further provides anon-transitory storage medium. Optionally, in one embodiment, theforegoing storage medium may store program code, the program code beingused for performing steps of the data processing method provided in theforegoing method embodiments.

Optionally, in one embodiment, the foregoing storage medium may belocated in any computer terminal in a computer terminal group in acomputer network, or located in any mobile terminal in a mobile terminalgroup.

Optionally, in one embodiment, the storage medium is configured to storeprogram code for performing the followings: obtaining sample data ofevent execution of a game client; performing preprocessing on the sampledata to obtain a plurality of layers of data combinations, each layer ofthe plurality of layers of data combinations corresponding to a targetevent object in a same target event, different layers of the pluralityof layers of data combinations corresponding to different target eventobjects in the target event, and the target event objects being eventobjects on the game client to be executed concurrently; processingsample information on each layer of data combination according to apreset processing algorithm, to obtain a processing result of each layerof data combination; and performing consolidation processing on theprocessing result of each layer of data combination to obtain a targetinstruction, the target instruction being used for instructing the gameclient to concurrently execute the different target event objectscorresponding to the different layers of data combinations.

Optionally, the storage medium is further configured to store programcode for performing the followings. tagging the sample data according toa plurality of sample sequences of the sample data, to obtain taggedsample data; performing preprocessing on the tagged sample data toobtain the plurality of layers of data combinations, different layers ofthe plurality of layers of data combinations corresponding to differentprocessing algorithms and different sample information; and performingprocessing on the sample information on each layer of data combinationaccording to the processing algorithm corresponding to each layer of theplurality of layers of data combinations, to obtain the processingresult of each layer of data combination.

Optionally, the storage medium is further configured to store programcode for performing the followings. determining a priority of each ofthe plurality of sample sequences; sequentially tagging each samplesequence according to the priority by using a tagging frame, to obtain aplurality of tagged sample sequences; combining neighboring taggedsample sequences of the plurality of tagged sample sequences accordingto a same tagging frame, to obtain a combined tagged sample sequence;and tagging a start frame and an end frame of the combined tagged samplesequence, to obtain the tagged sample data.

Optionally, the storage medium is further configured to store programcode for performing the followings. extracting, by using a preset statefunction, different state information of execution of a current eventobject of the game client from the tagged sample data, the current eventobject being an event object currently executed by the game client; andassembling the different state information, to obtain the plurality oflayers of data combinations.

Optionally, the storage medium is further configured to store programcode for performing the followings. before the performing processing onthe sample information on each layer of data combination according tothe processing algorithm corresponding to each layer of the plurality oflayers of data combinations, to obtain the processing result of eachlayer of data combination, obtaining character data on the game client;mapping the state information and the character data to a presetprocessing model according to a preset mapping system, to obtain eventdata of the target event; and generating the sample informationaccording to the state information, the character data and the eventdata.

Optionally, the storage medium is further configured to store programcode for performing the followings. after the generating the sampleinformation according to the state information, the character data andthe event data, performing rotation processing on the sample data, toextend the sample quantity corresponding to the sample data; and addingpreset information to sample information of the extended sample data.

Optionally, the storage medium is further configured to store programcode for performing the followings. performing processing on the sampleinformation on each layer of data combination according to a presetprobability model corresponding to each layer of data combination, toobtain an execution probability that is of execution of the target eventof the game client and that corresponds to each layer of datacombination; and performing processing on the sample information on eachlayer of data combination according to a preset value modelcorresponding to each layer of data combination, to obtain an executionvalue that is of execution of the target event of the game client andthat corresponds to each layer of data combination, the obtaining aprocessing result of each layer of data combination and performingconsolidation processing on the processing result of each layer of datacombination to obtain a target instruction including: performingconsolidation processing on the execution probability corresponding toeach layer of data combination and the execution value corresponding toeach layer of data combination, to obtain the target instruction.

Optionally, the storage medium is further configured to store programcode for performing the followings. after the obtaining a processingresult of each layer of data combination and performing consolidationprocessing on the processing result of each layer of data combination toobtain a target instruction, determining, according to a preset stateevaluation function, whether the target instruction needs to be updated;and updating the target instruction if it is determined that the targetinstruction needs to be updated.

Optionally, the storage medium is further configured to store programcode for performing the followings. after the obtaining a processingresult of each layer of data combination and performing consolidationprocessing on the processing result of each layer of data combination toobtain a target instruction, obtaining different target stateinformation during execution of the different target event objectsaccording to the target instruction by the game client; and updating theprocessing result of each layer of data combination according to thedifferent target state information, to obtain an updated processingresult of each layer of data combination, the obtaining a processingresult of each layer of data combination and performing consolidationprocessing on the processing result of each layer of data combination toobtain a target instruction including: obtaining the updated processingresult of each layer of data combination, and performing consolidationprocessing on the updated processing results of the plurality of layersof data combinations, to obtain an updated target instruction.

Optionally, for a specific example in one embodiment, refer to theexamples described in the foregoing embodiments. This is not describedin one embodiment again.

Optionally, in one embodiment, the storage medium may include any mediumthat may store program code such as a USB flash drive, a ROM, a RAM, aremovable hard disk, a magnetic disk, or an optical disc.

The update method of the data processing method, the apparatus, and thestorage medium according to of the present disclosure are describedabove with reference to the accompanying drawings by way of example.However, persons skilled in the art should understand that they may makevarious modifications to the update method, the apparatus, and thestorage medium of the virtual application attributes provided in thepresent disclosure without departing from the content of the presentdisclosure. Therefore, the protection scope of the present disclosureshall be subject to the appended claims.

When being implemented in the form of a software functional unit andsold or used as an independent product, the integrated unit in theforegoing embodiments may be stored in the foregoing computer-readablestorage medium. The computer software product is stored in a storagemedium and includes several instructions for instructing one or morecomputer devices (which may be a personal computer, a server, a networkdevice, or the like) to perform all or some of the steps of the methodsin the embodiments of the present disclosure.

In the foregoing embodiments of the present disclosure, the descriptionof each embodiment has respective focuses, and for the part that is notdetailed in an embodiment, refer to the relevant description of otherembodiments.

In the several embodiments provided in this application, it should beunderstood that the disclosed client may be implemented in othermanners. For example, the described apparatus embodiment is merelyexemplary. For example, the unit division is merely logical functiondivision and may be other division during actual implementation. Forexample, a plurality of units or components may be combined orintegrated into another system, or some features may be ignored or notperformed. In addition, the displayed or discussed mutual couplings ordirect couplings or communication connections may be implemented byusing some interfaces. The indirect couplings or communicationconnections between the units or modules may be implemented inelectronic or other forms.

The units described as separate parts may or may not be physicallyseparate, and parts displayed as units may or may not be physical units,may be located in one position, or may be distributed on a plurality ofnetwork units. Some or all of the units may be selected according toactual requirements to achieve the objective of the solutions in theembodiments.

In addition, functional units in the embodiments of the presentdisclosure may be integrated into one processing unit, or each of theunits may exist alone physically, or two or more units are integratedinto one unit. The integrated unit may be implemented in the form ofhardware, or may be implemented in the form of a software functionalunit.

The foregoing descriptions are merely exemplary implementations of thepresent disclosure. It should be noted that persons of ordinary skill inthe art may make several improvements or polishing without departingfrom the principle of the present disclosure and the improvements orpolishing shall fall within the protection scope of the presentdisclosure.

INDUSTRIAL PRACTICABILITY

In the embodiments of the present disclosure, sample data of eventexecution of a game client is obtained; preprocessing is performed onthe sample data to obtain a plurality of layers of data combinations,each layer of the plurality of layers of data combinations correspondingto a target event object in a same target event, different layers of theplurality of layers of data combinations corresponding to differenttarget event objects in the target event, and the target event objectsbeing event objects on the game client to be executed concurrently;processing is performed on each layer of data combination according to apreset processing algorithm, to obtain a processing result of each layerof data combination; and consolidation processing is performed on theprocessing result of each layer of data combination to obtain a targetinstruction. In this way, an objective of obtaining a target instructionby performing consolidation processing on the processing result of eachlayer of the plurality of layers of data combinations is achieved, and atechnical effect of improving data processing efficiency is achieved,thereby resolving a technical problem of a low data processingefficiency in the related art.

What is claimed is:
 1. A data processing method, comprising: obtainingsample data of event execution of a game client; performingpreprocessing on the sample data to obtain a plurality of layers of datacombinations, wherein each layer of the plurality of layers of datacombinations corresponds to a target event object in a same targetevent, different layers of the plurality of layers of data combinationscorrespond to different target event objects in the target event, andthe target event objects are event objects on the game client to beexecuted concurrently; performing processing on each layer of datacombinations according to a preset processing algorithm, to obtain aprocessing result of each layer of data combinations; and performingconsolidation processing on the processing result of each layer of datacombinations to obtain a target instruction, wherein the targetinstruction is used for instructing the game client to concurrentlyexecute the different target event objects corresponding to thedifferent layers of data combinations.
 2. The method according to claim1, wherein: the performing preprocessing on the sample data to obtain aplurality of layers of data combinations comprises: tagging the sampledata according to a plurality of sample sequences of the sample data, toobtain tagged sample data; and performing preprocessing on the taggedsample data to obtain the plurality of layers of data combinations,wherein different layers of the plurality of layers of data combinationscorrespond to different processing algorithms and different sampleinformation; and the performing processing on each layer of datacombinations according to a preset processing algorithm, to obtain aprocessing result of each layer of data combinations comprises:performing processing on the sample information on each layer of datacombinations according to the processing algorithm corresponding to eachof the plurality of layers of data combinations, to obtain theprocessing result of each layer of data combinations.
 3. The methodaccording to claim 2, wherein the tagging the sample data according to aplurality of sample sequences of the sample data, to obtain taggedsample data comprises: determining a priority of each of the pluralityof sample sequences; sequentially tagging each sample sequence accordingto the priority by using a tagging frame, to obtain a plurality oftagged sample sequences; combining neighboring tagged sample sequencesof the plurality of tagged sample sequences according to a same taggingframe, to obtain a combined tagged sample sequence; and tagging a startframe and an end frame of the combined tagged sample sequence, to obtainthe tagged sample data.
 4. The method according to claim 2, wherein theperforming preprocessing on the tagged sample data to obtain theplurality of layers of data combinations comprises: extracting, by usinga preset state function, different state information of execution of acurrent event object of the game client from the tagged sample data, thecurrent event object being an event object currently executed by thegame client; and assembling the different state information, to obtainthe plurality of layers of data combinations.
 5. The method according toclaim 4, wherein, before performing processing on the sample informationon each layer of data combinations according to the processing algorithmcorresponding to each layer of the plurality of layers of datacombinations, the method further comprises: obtaining character data onthe game client; mapping the state information and the character data toa preset processing model according to a preset mapping system, toobtain event data of the target event; and generating the sampleinformation according to the state information, the character data, andthe event data.
 6. The method according to claim 5, wherein, after thegenerating the sample information according to the state information,the character data, and the event data, the method further comprises:performing rotation processing on the sample data, to extend a samplequantity corresponding to the sample data; and adding preset informationto sample information of the extended sample data.
 7. The methodaccording to claim 2, wherein: the performing processing on the sampleinformation on each layer of data combinations according to theprocessing algorithm corresponding to each layer of the plurality oflayers of data combinations, to obtain the processing result of eachlayer of data combinations comprises: performing processing on thesample information on each layer of data combinations according to apreset probability model corresponding to each layer of datacombinations, to obtain an execution probability of execution of thetarget event of the game client and corresponding to each layer of datacombinations; and performing processing on the sample information oneach layer of data combinations according to a preset value modelcorresponding to each layer of data combinations, to obtain an executionvalue that is of execution of the target event of the game client andthat corresponds to each layer of data combinations, and the performingconsolidation processing on the processing result of each layer of datacombinations to obtain a target instruction comprises: performingconsolidation processing on the execution probability corresponding toeach layer of data combinations and the execution value corresponding toeach layer of data combinations, to obtain the target instruction. 8.The method according to claim 1, wherein, after performing consolidationprocessing on the processing result of each layer of data combinationsto obtain a target instruction, the method further comprises:determining, according to a preset state evaluation function, whetherthe target instruction needs to be updated; and updating the targetinstruction when it is determined that the target instruction needs tobe updated.
 9. The method according to claim 1, wherein: afterperforming consolidation processing on the processing result of eachlayer of data combinations to obtain a target instruction, the methodfurther comprises: obtaining different target state information duringexecution of the different target event objects according to the targetinstruction by the game client; and updating the processing result ofeach layer of data combinations according to the different target stateinformation, to obtain an updated processing result of each layer ofdata combinations, the performing consolidation processing on theprocessing result of each layer of data combinations to obtain a targetinstruction comprises: obtaining the updated processing result of eachlayer of data combinations, and performing consolidation processing onthe updated processing results of the plurality of layers of datacombinations, to obtain an updated target instruction.
 10. A dataprocessing system, comprising: a memory storing computer programinstructions; and one or more processors coupled to the memory and, whenexecuting the computer program instructions, configured to perform:obtaining sample data of event execution of a game client; performingpreprocessing on the sample data to obtain a plurality of layers of datacombinations, wherein each layer of the plurality of layers of datacombinations corresponds to a target event object in a same targetevent, different layers of the plurality of layers of data combinationscorrespond to different target event objects in the target event, andthe target event objects are event objects on the game client to beexecuted concurrently; performing processing on each layer of datacombinations according to a preset processing algorithm, to obtain aprocessing result of each layer of data combinations; and performingconsolidation processing on the processing result of each layer of datacombinations to obtain a target instruction, wherein the targetinstruction is used for instructing the game client to concurrentlyexecute the different target event objects corresponding to thedifferent layers of data combinations.
 11. The data processing systemaccording to claim 10, wherein: the performing preprocessing on thesample data to obtain a plurality of layers of data combinationscomprises: tagging the sample data according to a plurality of samplesequences of the sample data, to obtain tagged sample data; andperforming preprocessing on the tagged sample data to obtain theplurality of layers of data combinations, wherein different layers ofthe plurality of layers of data combinations correspond to differentprocessing algorithms and different sample information; and theperforming processing on each layer of data combinations according to apreset processing algorithm, to obtain a processing result of each layerof data combinations comprises: performing processing on the sampleinformation on each layer of data combinations according to theprocessing algorithm corresponding to each of the plurality of layers ofdata combinations, to obtain the processing result of each layer of datacombinations.
 12. The data processing system according to claim 11,wherein the tagging the sample data according to a plurality of samplesequences of the sample data, to obtain tagged sample data comprises:determining a priority of each of the plurality of sample sequences;sequentially tagging each sample sequence according to the priority byusing a tagging frame, to obtain a plurality of tagged sample sequences;combining neighboring tagged sample sequences of the plurality of taggedsample sequences according to a same tagging frame, to obtain a combinedtagged sample sequence; and tagging a start frame and an end frame ofthe combined tagged sample sequence, to obtain the tagged sample data.13. The data processing system according to claim 11, wherein theperforming preprocessing on the tagged sample data to obtain theplurality of layers of data combinations comprises: extracting, by usinga preset state function, different state information of execution of acurrent event object of the game client from the tagged sample data, thecurrent event object being an event object currently executed by thegame client; and assembling the different state information, to obtainthe plurality of layers of data combinations.
 14. The data processingsystem according to claim 13, wherein, before performing processing onthe sample information on each layer of data combinations according tothe processing algorithm corresponding to each layer of the plurality oflayers of data combinations, the one or more processors are furtherconfigured to perform: obtaining character data on the game client;mapping the state information and the character data to a presetprocessing model according to a preset mapping system, to obtain eventdata of the target event; and generating the sample informationaccording to the state information, the character data, and the eventdata.
 15. The data processing system according to claim 14, wherein,after the generating the sample information according to the stateinformation, the character data, and the event data, the one or moreprocessors are further configured to perform: performing rotationprocessing on the sample data, to extend a sample quantity correspondingto the sample data; and adding preset information to sample informationof the extended sample data.
 16. The data processing system according toclaim 11, wherein: the performing processing on the sample informationon each layer of data combinations according to the processing algorithmcorresponding to each layer of the plurality of layers of datacombinations, to obtain the processing result of each layer of datacombinations comprises: performing processing on the sample informationon each layer of data combinations according to a preset probabilitymodel corresponding to each layer of data combinations, to obtain anexecution probability of execution of the target event of the gameclient and corresponding to each layer of data combinations; andperforming processing on the sample information on each layer of datacombinations according to a preset value model corresponding to eachlayer of data combinations, to obtain an execution value that is ofexecution of the target event of the game client and that corresponds toeach layer of data combinations, and the performing consolidationprocessing on the processing result of each layer of data combinationsto obtain a target instruction comprises: performing consolidationprocessing on the execution probability corresponding to each layer ofdata combinations and the execution value corresponding to each layer ofdata combinations, to obtain the target instruction.
 17. The dataprocessing system according to claim 10, wherein, after performingconsolidation processing on the processing result of each layer of datacombinations to obtain a target instruction, the one or more processorsare further configured to perform: determining, according to a presetstate evaluation function, whether the target instruction needs to beupdated; and updating the target instruction when it is determined thatthe target instruction needs to be updated.
 18. The data processingsystem according to claim 10, wherein: after performing consolidationprocessing on the processing result of each layer of data combinationsto obtain a target instruction, the one or more processors are furtherconfigured to perform: obtaining different target state informationduring execution of the different target event objects according to thetarget instruction by the game client; and updating the processingresult of each layer of data combinations according to the differenttarget state information, to obtain an updated processing result of eachlayer of data combinations, the performing consolidation processing onthe processing result of each layer of data combinations to obtain atarget instruction comprises: obtaining the updated processing result ofeach layer of data combinations, and performing consolidation processingon the updated processing results of the plurality of layers of datacombinations, to obtain an updated target instruction.
 19. Anon-transitory computer-readable storage medium storing computer programinstructions executable by at least one processor to perform: obtainingsample data of event execution of a game client; performingpreprocessing on the sample data to obtain a plurality of layers of datacombinations, wherein each layer of the plurality of layers of datacombinations corresponds to a target event object in a same targetevent, different layers of the plurality of layers of data combinationscorrespond to different target event objects in the target event, andthe target event objects are event objects on the game client to beexecuted concurrently; performing processing on each layer of datacombinations according to a preset processing algorithm, to obtain aprocessing result of each layer of data combinations; and performingconsolidation processing on the processing result of each layer of datacombinations to obtain a target instruction, wherein the targetinstruction is used for instructing the game client to concurrentlyexecute the different target event objects corresponding to thedifferent layers of data combinations.
 20. The non-transitorycomputer-readable storage medium according to claim 19, wherein: theperforming preprocessing on the sample data to obtain a plurality oflayers of data combinations comprises: tagging the sample data accordingto a plurality of sample sequences of the sample data, to obtain taggedsample data; and performing preprocessing on the tagged sample data toobtain the plurality of layers of data combinations, wherein differentlayers of the plurality of layers of data combinations correspond todifferent processing algorithms and different sample information; andthe performing processing on each layer of data combinations accordingto a preset processing algorithm, to obtain a processing result of eachlayer of data combinations comprises: performing processing on thesample information on each layer of data combinations according to theprocessing algorithm corresponding to each of the plurality of layers ofdata combinations, to obtain the processing result of each layer of datacombinations.